HEALTHCARE DATA ANALYTICSUnlocking The Power Of Healthcare Data TOGETHERLisa Lix, PhD, P.Stat.September 30, 2015Cyber Summit Generation D: Data Scientists of Tomorrow

OutlineWhy together is better

• Where I work and do my research• Examples of healthcare analytics in

action• What lies ahead

George and Fay Yee Centre for Healthcare Innovation (CHI)• CHI is a partnership between the University of

Manitoba and the Winnipeg Regional Health Authority

• CHI brings together leaders and practitioners from many academic disciplines and areas of practice

• CHI aims to:– improve patient outcomes, – enhance patient experiences, and – improve access to care for Manitobans

CHI’s PlatformsData

Science

Evaluation

Knowledge Synthesis

Health System

Performance

Clinical Trials

Project Manageme

nt

Knowledge Translation

Patient

CHI’s Data Science Platform

Our Activities:Research

CollaborationTraining

Clinical Research

Data Group

Biostatistics Group

Bioinformatics and

Computational Biology

Group

Our Vision: To create and integrate diverse types of patient data and develop and apply the best analytic methods to provide new insights about patient outcomes, experiences, and care

Healthcare Analytics in Action: Provincial

Manitoba Centre for Health Policy Data Quality Framework

Manitoba Centre for Health Policy Research Data Repository

Population- Based Health

Registry

Social Housing

Education

Healthy Child

Manitoba

Immunization

Medical Services

Lab

Nursing Home

Clinical

ProviderVital Statistics

Emergency Dept.

Health Links

Home Care

Pharmaceuticals

Hospital

Family Services

Income Assistance

Census Data

• Family First

• Healthy Baby

• Intensive Care Unit

• Fetal Alcohol Spectrum Disorder

• Pediatric Diabetes

Automating Data Quality Assessments

Healthcare Analytics in Action: National• The Canadian Chronic Disease

Surveillance System (CCDSS)

• The Canadian Network of Observational Drug Effect Studies (CNODES)

Healthcare falls primarily under the authority of the provinces and territories

The provincial and territorial healthcare systems differ in structure and operation

This results in a patchwork of systems and data resources

The Canadian Healthcare System

The Canadian Chronic Disease Surveillance System (CCDSS) Established by the Public Health Agency of

Canada (PHAC) as a collaborative initiative amongst the federal, provincial, and territorial governments

Uses health administrative data to estimate chronic disease prevalence/incidence and the related burden on the healthcare system

Adopts a distributed surveillance system model that respects the data custodial responsibilities of the provinces and territories

Provides a standardized pan-Canadian approach to chronic disease surveillance

Multimorbidity: An Example

Note: The 95% Confidence Intervals shows an estimated range of values which is likely to include the statistic 19 times out of 20. Data Source: Public Health Agency of Canada: using CCDSS data files contributed by the provinces and territories as of August 2015

Note: The 95% Confidence Intervals shows an estimated range of values which is likely to include the statistic 19 times out of 20

CCDSS Data

CCDSS StructureModelPHAC receives input and guidance from provincial/territorial reps under federal/provincial/territorial agreements

Canadian Network of Observational Drug Effect Studies (CNODES)• Network of over 60 Canadian

pharmacoepidemiologists, biostatisticians, clinicians, clinical pharmacologists, pharmacists, IT professionals, data analysts, and students using linked administrative data in 7 provinces plus UK and US data

• Timely responses to queries from Canadian public stakeholders about drug safety and effectiveness

Britis

h Colu

mbiaAlb

erta

1 -

2 -

Nova S

cotia

*CPR

DMark

etSca

nCNODES

Quebe

c

Ontario

Manito

ba

Sask

atche

wan

CNODES Sites

CNODES Database Model• Data partners maintain physical control

of their data• Local content experts maintain a close

relationship with the data• Eliminates the need to create, secure,

maintain and manage access to a central, complex data warehouse

• Gives a pan-Canadian meta-analysis “answer” that dramatically increases sample size for rare events, RAPID RESPONSE

CNODES ProjectIsotretinoin Use Amongst Women of Reproductive Age and the Risk of Pregnancy and Adverse Pregnancy Outcomes

Population basedMulti-province participationPregnancy/Outcomes in isotretinoin usersUS Comparisons

CNODES Project

What Lies Ahead?• Data Linkage

– Images– Streaming data from wearable devices– Electronic medical records

• Analyses– Biases– Rare events

• Data Visualization• Formalized Training

Training in Healthcare Analytics• Strategic:

– Focussed on performance – Strategic thinking and communication skills– Less essential to have skills in the technical, nitty-gritty

details of setting up database systems and defining or selecting algorithms

• Operational: – Training in programming, statistics, mathematics– Skills in implementing systems to probe and interpret

data

Essential Skills • Constructing data queries• Manipulating data into different

formats or structures• Modeling & analysis• Telling the story of the data

The Science of Data Quality

Aim for Smart Data, Not Necessarily Big Data

Contact Information

Lisa Lix, PhD P.Stat.e-mail: lisa.lix@umanitoba.ca